Cardiography system and method using automated recognition of hemodynamic parameters and waveform attributes

ABSTRACT

A cardiography system and method using automated recognition of hemodynamic parameters and waveform attributes is provided. The cardiography system and method includes at least one sensor, a knowledge base and a processing device. The at least one sensor provides a waveform signal and a hemodynamic parameter input. The knowledge base includes data corresponding to various disease states. The processing device receives the waveform signal and hemodynamic parameter input from the sensor, identifies waveform attributes on the waveform signal, measures the waveform attributes, accesses the knowledge base, cross-references the waveform attributes and the hemodynamic parameters with data in the knowledge base, and outputs a suggested likelihood of a particular disease state. The knowledge base optionally includes goal-directed therapies associated with particular disease states for providing suggested goal-directed therapies based on the cross-referencing of the waveform attributes and the hemodynamic parameters with the knowledge base.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method and system for measuring andreporting time based parameters associated with heart activity. Moreparticularly, the present invention relates to a cardiography system andmethod using automated recognition of hemodynamic parameters andwaveform attributes for monitoring and recording signals derived fromheart valve activity and guiding goal-directed therapy by correlatingcardiovasculograms (CVG) with waveform and hemodynamic data stored inlocal memory.

2. Discussion of the Related Art

Cardiac output and circulatory flow are a balance of the pumping abilityof the heart. Congestive heart failure (CHF) is a condition in which thecardiac output is unable to meet the metabolic demands of the body. Thiscondition can vary in severity from a simple elevation in cardiacfilling pressures, known as compensated failure, to severe hypoxia andedema, known as decompensated failure.

CHF is thought to result from a failure in the contractile elements ofthe heart during the systolic phase of the cardiac cycle, which is knownas systolic congestive heart failure. Systolic CHF is characterizedclinically by an ejection fraction of less than 30%. Systolic CHF can bea result of a myriad of possible pathologies affecting the contractileability of the heart muscle including myocardial infarction,cardiomyopathies, and metabolic disorders. Management of this disorderhas evolved over recent years and is highly dependent upon the severityof the condition. Most treatment regimens involve attempting to increasesystolic contractility or focusing on hemodynamic manipulations thatallow the heart to take a passive role in circulatory control.

More recently it has been recognized that limitations in cardiac fillingand venous return during diastole can also result in an abnormalcirculatory flow, which is known as diastolic congestive heart failure.Diastolic CHF is defined as the condition in which there is evidence ofthe clinical signs of CHF in the presence of normal systolicfunctioning. This condition occurs in as many as 30% of patientspresenting with heart failure. Some of this dysfunction may be due to astiff myocardium limiting the passive phase of diastolic filling.However, the majority of dysfunction is caused by lengthening ofisovolumic myocardial relaxation or isovolumic relaxation times (IVRT).Myocardial relaxation is an energy-dependent, active process that ismainly unconstrained by preload and afterload considerations.Ventricular hypertrophy is often the end result of long-standinghypertension and is commonly responsible for delays in IVRT due toabnormalities in calcium kinetics. Researchers have shown that an IVRTgreater than 0.125 sec is indicative of diastolic dysfunction. Patientspresenting with CHF due to diastolic dysfunction may not respond totraditional therapies. These traditional therapies can even bedetrimental to patients presenting with CHF due to diastolicdysfunction. Patients with evidence of acute decompensation secondary toa diastolic mechanism may have worsening of symptoms, hypotensiveresponse, and reduced cardiac output with the typical off-loadingtreatments of diuretics or preload reducing medication. As a result, itis important to identify accurately which type of CHF a patient ispresenting, in order to identify appropriate goal directed therapies.

The analysis of waveforms obtained from physiologic monitoring is acommon practice in medicine. Clinicians have used waveform patternsobtained from electrocardiography, capnography, cardiotocography, andspirometry to assist in the diagnostic assessment of patient pathology.The unassisted, human interpretation of CVG pattern recognition anddifferentiation of these waveforms is a clinical art form that requiresexperience and skilled expertise. However, automated computerizedinterpretations of waveforms based upon specific segmental waveformcriteria have been widely used in medicine to assist clinicians in thediagnostic process. In the field of electrocardiography, theinterpretative waveform criteria have been developed based upon evidencefrom clinical correlations and standardized for specific diagnoses.Proprietary computerized algorithms use these criteria for theirelectrocardiographic interpretation.

Clinical evidence supports the use of waveform analysis and diagnosticinterpretation in the field of impedance cardiography (ICG). ICG is atechnique used to provide non-invasive monitoring and analysis of apatient's cardiac performance. ICG systems measure and report severaltime-based parameters related to cardiac performance, including thepre-ejection period (PEP) and the left ventricular ejection time (LVET).ICG systems produce ICG signals from monitoring movement and volume ofblood as a result of the heart contracting. Exemplary ICG systems areshown and described in Ackmann et al., U.S. Pat. No. 5,178,154; andReining, U.S. Pat. No. 5,505,209 both incorporated by reference hereinin their entireties. The '154 and '209 patents disclose the use ofelectrode bands placed on a patient with high frequency, low magnitudeelectrical current applied to the electrode bands. Voltage changesacross the bands are read, filtered and converted into thoracicimpedance. The ICG system displays the thoracic impedance signal versustime to create a visual display of the ICG signal. The '154 patentfurther discloses that ICG systems can receive conventionalelectrocardiograph signals, signals from blood pressure monitors,signals from piezoelectric microphones attached to the chest of thepatient and the like. These signals, in addition to thoracic impedance,can be stored and averaged via a memory storage device connected to theICG system.

A CVG is a waveform produced by the processing of impedance cardiography(ICG) signals and which may also be supported by processing other signalinputs such as electrocardiography (ECG) signals, phonocardiography(PCG) signals and other hemodynamic signals. The CVG waveform incombination with the accompanying electrocardiograph, describe theelectromechanical events of the cardiac cycle. The CVG is a signaturewaveform and can be interpreted by physicians in much the same way aselectrocardiograms are interpreted. Despite improvements in ICG systemsand/or signal processing, there have been no advances in the methodologyof automated waveform analysis for ICG systems. Specifically, thereexists a need for using CVG waveforms in an automated system todifferentiate decompensated heart failure from other common clinicalconditions and to further distinguish between diastolic and systolicforms of heart failure.

Phonocardiography (PCG) is a non-invasive technique used by healthcareprofessionals to monitor cardiac performance. PCG systems generate PCGsignals by monitoring the opening and closing of valves within apatient's heart. PCG systems use a microphone that records sounds ofheart valve activity, similar to electronic stethoscopes known in theart, in order to provide indications of aortic heart valve opening(shown as S1 on FIG. 1) and aortic heart valve closure (shown as S2 onFIG. 1).

Another non-invasive system used to monitor heart activity is anelectrocardiogram (ECG) system. ECG signals are electrical signals thatare generated from the depolarization and repolarization of myocardialcells in a patient's heart. ECG systems are known to include a firstexternal electrode attached to a patient's skin, a second externalelectrode attached to a patient's skin and optionally a third externalelectrode attached to a patient's skin. An amplifier is used to monitorelectrical heart activity signals at the first and second electrodes andgenerate an ECG signal based on the difference between these activitysignals. The optional third electrode can be used to reduce or offsetnoise in the ECG signal.

Still another non-invasive system used by healthcare professionals tomonitor cardiac performance is a blood pressure system. A patient'sblood pressure is monitored according to known techniques and convertedinto a blood pressure signal. The blood pressure signal is thendisplayed on a blood pressure waveform. Blood pressure waveforms,similar to PCG waveforms, can be used by healthcare professionals toidentify heart valve closure because the dicrotic notch in bloodpressure waveforms reflects closure of the aortic heart valve. Otherexemplary systems using signals that have pulsatile characteristicsresulting from the contraction of the heart are shown and described inKimball et al., U.S. Pat. No. 6,763,256, herein incorporated byreference in its entirety.

The PEP is defined as the period of isovolumic ventricular contractionwhen the patient's heart is pumping against the closed aortic valve. InICG systems, the PEP is measured starting with the initiation of the QRScomplex (the “Q” point on FIG. 1) of the ECG signal and ending with thestart of the mechanical systole as marked by the initial deflection ofthe systolic waveform (the “B” point on FIG. 1) of the ICG signalcoincident with the opening of the aortic valve or the onset of leftventricular ejection into the aorta. The LVET begins at the end of thePEP and ends at the closure of the aortic valve (the “X” Point onFIG. 1) when ejections ends.

It is important that ICG systems provide accurate results for the PEPand the LVET because healthcare professionals utilize the results ofthese parameters when making decisions about patient diagnosis and care.Additionally, accurate determination of the PEP and the LVET timeintervals is also required for accurate and reliable determination ofsubsequent and dependent parameters. For example, results fromdetermination of the PEP and the LVET are used to calculate the systolictime ratio (STR), where STR=PEP/LVET. While many ICG systems useproprietary equations for determination of stroke volume (SV), it iscommonly known that SV equations frequently incorporate LVET as an inputparameter. Accordingly, accurate determination of time intervals betweenthe PEP and the LVET is also necessary for accurate determination of SV,and subsequently for cardiac output (CO) based on SV and heart rate(HR), where CO=SV*HR.

Many CVG waveforms, particularly for healthy individuals, providesufficient detail so that ICG systems can identify the location of theaortic valve opening and closing, or the LVET, with a high degree ofconfidence. For example, in the CVG waveform depicted in FIG. 1,opening, B point, of the aortic valve and closing, X point, of theaortic valve are easily identifiable. When comparing the CVG waveformwith the phonocardiograph (PCG) waveform (both shown in FIG. 1), markingof the B point in the CVG waveform is confirmed by the time-associatedpresence of the S1 component in the PCG waveform. Similarly, marking ofthe X point in the CVG waveform is confirmed by the time associatedpresence of the S2 component in the PCG waveform.

A number of parameters, including but not limited to cardiac output,thoracic fluid content, Heather Index, and the like, have been derivedfrom impedance signals to assist in the diagnosis of decompensated heartfailure. Traditionally, however, ICG systems only analyze attributes ofthe impedance signal when determining the location of heart valveactivity. Some ICG systems may record and display PCG signals, bloodpressure signals, and/or other signals having pulsatile characteristicsresulting from contraction of the heart, but these ICG systems do notintegrate these signals into the automatic location of heart valveactivity. ICG systems alone often lack sufficient information toaccurately and reliably determine the PEP and the LVET because ofconfounding information related to opening and closing of the patient'saortic valve. For example, in the CVG waveform depicted in FIG. 2,closure, X point, of the aortic valve could be any of severaldepressions following the peak blood flow, C. The known algorithmselected the deepest depression in the CVG waveform because the aorticvalve closure is often thought to produce the strongest negative signal.However, when the CVG waveform depicted in FIG. 2 is compared with thePCG waveform depicted in FIG. 2, the aortic valve closure, X point,should have been one of the later depressions in the CVG waveform inorder to correlate with the time associated presence of the S2 componentin the PCG waveform. Accordingly, there is a need for an impedancecardiography method and system for automated correlation of impedancesignals from ICG systems with other signals derived from heart valveactivity in order to provide more accurate identification of heart valveactivity.

Many of the specific segmental criteria used in this comprehensivepattern recognition are based upon well-established characterizations ofchanges in systolic and diastolic function as determined from elementsof the impedance cardiogram.

It is known that experienced healthcare professionals can recognize, ordiagnose, certain disease states by analyzing hemodynamic parameters incombination with visual displays of ICG signals provided by some ICGsystems. Experienced healthcare professionals can easily recognize thesystolic and diastolic segments of these visual displays in addition toother attributes such as amplitude, shape, tone, slope and timing, incombination with hemodynamic parameters. Analysis of these attributesallows experienced healthcare professionals to ascertain an underlyingdisease state. However, variations in ICG signal attributes makesnon-automated diagnosis difficult.

It is also known that some ICG systems provide minimal waveforminformation. When using these types of systems, healthcare professionalsmust rely largely on numeric parameters to make a diagnosis becausethese systems do not provide other information. With ICG systems that donot display waveforms, even experienced healthcare professionals may beunable to make a diagnosis. Based on the foregoing, there exists a needfor an automated cardiography method and system for measuringcardiovasculograms that provides suggested underlying conditions basedon correlating the recognized waveform attributes and hemodynamicparameters with waveform attributes and hemodynamic parametersassociated with particular underlying conditions.

SUMMARY OF THE INVENTION

The present invention provides a cardiography method and system formeasuring cardiovasculograms including signals derived from heart valveactivity that are time coordinated with ICG signals, such that thesignals derived from heart valve activity are used as confirmation thatthe cardiography system is accurately positioning heart valve activity.The present invention also provides improved accuracy in reported valuessuch as PEP, LVET, STR, SV and CO. The present invention also providesimproved accuracy of graphic presentation of heart activity when thegraphic presentation includes identifying heart valve activity. Thepresent invention categorizes and saves waveform attributes andhemodynamic parameters correlated with various patient disease statessuch that measured waveform attributes and hemodynamic parameters can bematched with the categorized and saved data in order to provideautomated diagnoses. The present invention provides physicians withassistance in achieving goal directed therapy.

The present invention includes a cardiography system for automatedrecognition of hemodynamic parameters and waveform attributes includingone or more sensors for providing one or more waveform signals and ahemodynamic parameter input; a knowledge base for providing datacorresponding to various disease states; a processing device connectedto the sensor(s) and the knowledge base, where the processing devicereceives the waveform signal(s) and the hemodynamic parameter input,identifies waveform attributes on the waveform signal, measures thewaveform attributes, measures the hemodynamic parameter input,cross-references the waveform attributes and the hemodynamic parameterinput with the knowledge base, and outputs a suggested likelihood of aparticular disease state based on the cross-referencing. The system inaccordance with the present invention optionally includes a displaydevice for displaying the output. The knowledge base of the presentinvention can also include goal-directed therapies associated withparticular disease states for providing suggested goal-directedtherapies based on the cross-referencing of the waveform attributes andthe hemodynamic parameters with the knowledge base.

The present invention also includes a method for automated recognitionof hemodynamic parameters and waveform attributes to assess diseasestates including the steps of providing one or more sensor forgenerating one or more waveform signal and a hemodynamic parameterinput; providing a knowledge base having data corresponding to variousdisease states; providing a processing device in communication with thesensor(s) and the knowledge base, where the processing device is usedfor receiving the waveform signal(s) and the hemodynamic parameterinput, identifying waveform attributes on the waveform signal, measuringthe waveform attributes, measuring the hemodynamic parameter input,accessing the knowledge base, cross-referencing the waveform attributeswith data in the knowledge base, cross-referencing the hemodynamicparameter input with data in the knowledge base, and outputting asuggested likelihood of a particular disease state based on thecross-referencing step. The method in accordance with the presentinvention optionally includes a display device for displaying theoutput. The knowledge base of the present invention can also includegoal-directed therapies associated with particular disease states forproviding suggested goal-directed therapies based on thecross-referencing of the waveform attributes and the hemodynamicparameters with the knowledge base.

The invention will be further described with reference to the followingdetailed description taken in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 represents CVG and PCG waveforms of a healthy patient;

FIG. 2 represents CVG and PCG waveforms of an unhealthy patient;

FIG. 3 is a schematic diagram of the system of the present inventionillustrating the principal components thereof;

FIG. 4 represents common ECG and ICG signals;

FIG. 5 represents a correlation between an O/C ratio, which is derivedfrom an impedance cardiogram, and invasively measured pulmonarycapillary wedge pressure (PCWP);

FIG. 6 represents a correlation between the change in baseline thoracicimpedance Z₀ and the change in the central venous pressure;

FIG. 7 represents a correlation between degree of severity of leftventricular hypertrophy (LVH) and isovolumic relaxation time (IVRT);

FIG. 8 is a flowchart illustrating one method for usingcardiovasculogram (CVG) criteria for the diagnosis of heart failure;

FIG. 9 is a block diagram of the exemplary components of an electronicprocessing device used in accordance with the system of the presentinvention;

FIG. 10 represents an CVG waveform of a patient with systolic heartfailure;

FIG. 11 represents an CVG waveform of a patient with diastolic heartfailure;

FIG. 12 is a flowchart illustrating one method of correlating measuredcardiovasculograms with known cardiovasculograms in accordance with thepresent invention;

FIG. 13 represents the CVG waveform shown in FIG. 10 with additionalinformation from an experienced healthcare professional; and

FIG. 14 represents the CVG waveform shown in FIG. 11 with additionalinformation from an experienced healthcare professional.

DETAILED DESCRIPTION

Referring to FIGS. 1 and 2, there is shown a CVG waveform 12 and a PCGwaveform 14 in accordance with the system and method of the presentinvention. Both figures depict heart valve activity in CVG waveform 12.The PEP is determined by identifying the time period between thestarting point of the QRS complex based on an ECG signal, labeled as theQ point, and the starting point of the mechanical systole as marked bythe initial deflection of the systolic waveform based on the ECG signalcoincident with the opening of the aortic valve or the onset of leftventricular ejection into the aorta, labeled as the B point. The LVET isdetermined by identifying the time period between the end of the PEP andthe closure of the aortic valve when ejection ends, labeled as the Xpoint. Both figures also depict heart valve activity in PCG waveform 14,where known devices and methods are used to monitor and record soundsassociated with the aortic valve opening, labeled as S1, and closing,labeled as S2. While FIGS. 1 and 2 depict PCG waveforms, those skilledin the art can appreciate that waveforms generated from any signalsderived from heart valve activity can be depicted in relation to CVGwaveforms.

Referring to FIG. 3, one embodiment of the system in accordance with thepresent invention includes a display device 16 used to displaycardiovasculograms and a processing device 18. Processing device is usedto receive inputs from a sensor 20 hooked to a patient, generatecardiovasculograms and communicate with display device 16. Those skilledin the art can appreciate that display device 16 may include any type ofdevice for presenting visual information such as, for example, acomputer monitor or flat-screen display. Display device 16 may beequipped with user input devices, such as buttons for silencing audiblealarms, erasing visual alarms or a combination thereof.

In one embodiment, sensor 20 includes electrodes for measuring ICGsignals, PCG signals and ECG signals, microphones for measuring andrecording heart sounds, blood pressure monitors, signals representingcentral venous pressure, finger plethysmographs and the like. While FIG.3 depicts one sensor 20, in another embodiment more than one sensor 20is used. Here, a first sensor is used to convert physiological data froma patient being monitored to a waveform having particular waveformattributes representing the physiological data. The first sensor can bean ICG system, an ECG system, a PCG system, or a combination thereof. Anoutput generated from the first sensor can be a physical output,including but not limited to graphical display, printout, and the like.The output from the first sensor can alternately or simultaneously be anelectrical output signal configured to be received by processing device18. A second sensor is used to measure other hemodynamic parameters fromthe patient being monitored and convert them into a second output. Thesehemodynamic parameters include, but are not limited to thoracic fluidcontent (TFC), heart rate (HR), pre-ejection period (PEP), leftventricular ejection time (LVET), isovolumic relaxation time (IVRT),stroke volume (SV), cardiac output (CO), blood pressure, Heather Index(HI), and systemic vascular resistance (SVR). The second outputgenerated from the second sensor can be a physical output, including butnot limited to graphical display, printout, and the like. The secondoutput from the second sensor can alternately or simultaneously be anelectrical output signal configured to be received by processing device18. Those skilled in the art can appreciate that the number and use ofthe sensors can vary. Those skilled in the art can appreciate that thesystem in accordance with the present invention may include stationarysystems used in intensive care units or emergency rooms in hospitals, ormay comprise portable units for use by emergency medical technicians inambulances, at the scene of accidents, and when responding to otheremergency situations.

Processing device 18 includes cardiovasculogram criteria for thediagnosis of heart failure based upon changes noted in the normalcontours and dimensions of the typical cardiovasculogram waveform. Whileclinicians often use a subjective pattern recognition methodology fordetermination of aberrancy, the present invention includes objectivecriteria that can be utilized for a more exacting analysis. Theseobjective criteria are useful in the development of a computerizedalgorithmic analysis of cardiovasculogram waveforms. Those skilled inthe art can appreciate that the system may contain criteria fordiagnoses of other disease states.

Referring now to FIG. 4, there are shown waveform attributes, includingbaseline thoracic impedance (Z₀), atrial wave (A), aortic valve opening(B), maximum aortic flow (C) (also represented as dZ/dt_(max)), aorticvalve closing (X); pulmonic valve closing (Y), mitral valve opening (O);pre-ejection period (PEP); ventricular ejection time (VET), isovolumicrelaxation time (IVRT), and ventricular filling time (FT). Thesewaveform attributes can be used to build the cardiovasculogram criterionfor diagnosing heart failure based upon changes noted in the normalcontours and dimensions of typical cardiovasculogram waveforms.

Still referring to FIG. 4, the C-wave is the major upward deflection inimpedance seen during systolic phase of the cardiac cycle that peaks atthe point of dZ/dt_(max). It is seen as the first deflection frombaseline thoracic impedance (Z₀) after the A-wave, beginning with the Bpoint and ending with the X point. During systole, the form of theC-wave is based on the force of ventricular contraction and theresultant aortic pulse pressure wave generated when blood is transferredout of the ventricle and into the aorta. The dZ/dt_(max) point of theC-wave is correlated with the peak aortic blood flow. Systolic functionis generally defined by the shape, depth, and duration of the C-wave.Normal amplitudes for the C-wave will vary depending on the system usedbut may range from 1.05 to 2.70.

The O-wave is defined by the diastolic portion of the cardiac cycle andpeaks at the point of mitral valve opening, shown as the O point on FIG.4. The filling of the vena cava and pulmonary vein during the earlyphase of diastole results in the up-slope of the impedance signal. Theventricular filling phase begins when the tricuspid and mitral valvesopen. During the terminal portion of the 0-wave, there is an increase inthe impedance signal and a return to baseline thoracic impedance (Z₀) atthe end of diastole as the venous system empties into the heart.Accordingly, this waveform reflects the events of diastole, includingcardiac filling and venous return.

LVET begins at the end of the PEP when the aortic valve opens. The LVETends at the closure of the aortic valve when ejection ends as determinedby the dZ/dt waveform. A typical normal value for LVET is about 295±26msec.

IVRT is a measure of diastolic function and active ventricularrelaxation. IVRT is represented as the X to O period, which begins withthe aortic valve closure and ends at the point of the maximum seconddeflection. A typical normal value for IVRT is less than 125 msec.

Referring now to FIG. 5, there is shown one embodiment for specificcriteria used to interpret a heart failure waveform based on theforegoing attributes in accordance with the present invention. In oneembodiment, specific criteria for determination of the proportionalchanges in the C-wave and O-wave in a patient with decompensated heartfailure are based upon the correlation of the O/C ratio and thepulmonary capillary wedge pressure (PCWP). A typical normal range of theO/C ratio, i.e. 0.43±0.09, was correlated with a PCWP of about 10 to 12mmHg, which is within typical normal PCWP range. Increases in the O/Cratio greater than 0.6±0.12 indicate pathologic congestion. This levelof O/C ratio correlates with a PCWP of about 20 to 25 mmHg, which isconsidered the break point for the onset of pulmonary edema formation.Therefore, an O/C ratio of about greater than 0.6 can be used toindicate cardiopulmonary congestion as seen in acute decompensated heartfailure.

Referring now to FIG. 6 there is shown another embodiment for specificcriteria used to interpret a heart failure waveform in accordance withthe present invention. Systolic heart failure is typically due tofailure of in contraction strength of the myocardium during systole.Systolic contractile force can be viewed from a basic physicalperspective, such as force=mass×acceleration, where systolic contractileforce is defined as the amount of blood ejected, mass, times thevelocity at which it is ejected, acceleration. In a normal CVG waveform,cardiac systole manifests as a sharp peaking C-wave (as shown in FIG.4). The upslope of the C-wave and the length of the base of the LVETwave are both independently correlated with a general myocardialcontractile state. A CVG waveform pattern with a broad blunted C-wave ischaracteristic of general heart failure in a patient and can be used tohelp differentiate that condition. The typically normal values for theC-wave and LVET as previously discussed herein are used in thedetermination of aberrancy. A decompensated systolic heart failurecondition is expected to physiologically lead to congestion within thevenous side of the circulatory system. This congestion can be correlatedwith increasing thoracic fluid content and increasing baseline thoracicimpedance (Z₀) in the CVG waveform as depicted in FIG. 6. Large O-waveswith elevated peaks are common in CVG waveforms depicting decompensatedheart failure.

Referring now to FIG. 7, there is depicted another embodiment forspecific criteria used to interpret a heart failure waveform inaccordance with the present invention. A correlation between leftventricular hypertrophy measured in mV and active ventricular relaxationtime measured in seconds can be used when assessing diastolic heartfailure. Diastolic heart failure is caused by a limitation inventricular compliance and relaxation, resulting in a limitation incardiac filling during diastole. The diastolic IVRT can be measured fromthe O-wave of the CVG. Prolongation in the IVRT is indicated by ageneral pattern of a widening of the base of the O-wave, which suggestsa diagnosis of diastolic heart failure. As shown in FIG. 7, IVRT by CVGwaveform analysis can be correlated with the degree of left ventricularhypertrophy, a major determinant of diastolic dysfunction. When there isconcurrent venous congestion due to the delay in cardiac filling duringdiastole combined with the occurrence of decompensation, the O-wavepattern may also have an elevated peak. This combination of factorsprovides a general CVG waveform pattern characterized by an overallsubstantial and prolonged O-wave with a large area under the curve. Thistype of O-wave may be indicative of decompensated diastolic heartfailure. Normal values for the O-wave and IVRT, as previously discussed,can be used for determination of aberrancy.

As illustrated in the flowchart depicted in FIG. 8, one method for usingCVG criterion for the diagnosis of heart failure in accordance with thepresent invention includes: inputting an ICG signal 30; inputting an ECGsignal 32; compiling an R-wave triggered ensemble average based on ECGand ICG signals 33; producing a CVG waveform; identifying the C, orsystolic, wave 34; identifying the O, or diastolic, wave 35; measuringC-wave attributes 36; measuring other supporting parameters 37;measuring O-wave attributes 38; inputting knowledge base for heartfailure classification 39; cross-referencing C-wave and O-waveattributes and other supporting parameters with knowledge base 40;suggesting the likelihood of systolic heart failure 42; and suggestingthe likelihood of diastolic heart failure 44. Those skilled in the artcan appreciate that cross-referencing of the measured attributes withthe knowledge base can be accomplished by Bayesian probabilitystatistics, fuzzy logic, or other advanced mathematical techniques.While FIG. 8 shows the use of an R-wave triggered ensemble average,those skilled in the art can appreciate that other waveform averagingtechniques, non-averaged waveforms and/or various compilations ofwaveforms and/or multi-beat sequences of waveforms can be used inaccordance with the present invention. The term knowledge base as usedherein is defined as a database, a computer program, any type of datareadable by an electronic medium, any data (whether or notalpha-numeric) that can be indexed and stored in an electronic medium,data stored in hard copy that can be accessed by or entered into thesystem of the present invention by users, and the like.

In the flowchart depicted in FIG. 8, the C-wave attributes and O-waveattributes that could be measured in steps 36 and 38 include, but arenot limited to, amplitude, duration, upward slope, downward slope,shape, depth, area, tone and presence of additional peaks. Othersupporting parameters that could be measured in step 37 include, but arenot limited to, thoracic fluid content (TFC), heart rate (HR),pre-ejection period (PEP), left ventricular ejection time (LVET),systolic time ratio, isovolumic relaxation time (IVRT), stroke volume(SV), stroke volume index, cardiac output (CO), cardiac index, bloodpressure, Heather Index (HI), rate pressure product, ejection fraction,end diastolic volume, pulmonary artery occlusion pressure, centralvenous pressure and systemic vascular resistance (SVR). FIG. 8 depictsthe use of PCG signals and other supporting parameters to confirm heartvalve activity in the CVG waveform for illustrative purposes only. Thoseskilled in the art can appreciate that PCG signals and/or othersupporting parameters could be used alone or in combination to confirmheart valve activity in CVG waveforms.

In step 40, waveform attributes and other supporting parameters arecross-referenced against a knowledge base containing known attributes ofheart failure classifications. Cross-reference logic for identifyinglikelihood of systolic heart failure and diastolic heart failure,including assessing C-wave parameters, O-wave parameters, and supportingparameters, is included in processing device 18. In one embodiment, thelogic could also be used to assess cross-factors. One exemplarycross-factor is the ratio of the O-wave height to C-wave height. Thoseskilled in the art can appreciate that cross-referencing of the measuredattributes with the knowledge base can be accomplished by Bayesianprobability statistics, fuzzy logic, or other advanced mathematicaltechniques.

In one embodiment, the suggestion of the likelihood of systolic heartfailure in step 42 or diastolic heart failure in step 44 could bepresented with confidence information in a numeric, graphical, barpresentation, or other format. In another embodiment, the suggestion ofthe likelihood of systolic heart failure in step 42 or diastolic heartfailure in step 44 could be associated with the likelihood orcoincidence of waveform attributes being associated with a standardizedheart failure classification system such as the New York HeartAssociation (NYHA) classification system.

Referring now to FIG. 9, processing device 18 illustrates typicalcomponents of a processing device. Processing device 18 includes a localmemory 46, a secondary storage device 54, a processor 56, a userinterface device 60 and an output device 58. Local memory 46 may includerandom access memory (RAM) or similar types of memory, and it may storeone or more applications 48, including system software 50, and a webserver 52, for execution by processor 56. Local memory 46 is generallylocated in individual pieces of equipment used to monitor cardiacperformance of patients. Secondary storage device 54 may include a harddisk drive, floppy disk drive, CD-ROM drive, or other types ofnon-volatile data storage. The local cache that includes a patient's CVGdata may be stored on secondary storage device 54. Processor 56 mayexecute system software 50 and other applications 48 stored in localmemory 46 or secondary storage 54. Processor 56 may execute systemsoftware 50 in order to provide the functions described in thisspecification including, but not limited to, measuring, reporting,displaying and comparing cardiovasculograms. User interface device 60may include any device for entering information into processing device18, such as a keyboard, mouse, cursor-control device, touch-screen,infrared, microphone, digital camera, video recorder, or any otherinstrument or device necessary to measure, report, display and comparecardiovasculograms. Output device 58 may include any type of device forpresenting a hard copy of information, such as a printer, and othertypes of output devices including speakers or any device for providinginformation in audio form.

Web server 52 is used to provide access to patient data stored in memory46 and on secondary storage devices 54 and display the data. Web server52 allows users secure remote access to the system through which theycan monitor the status of a patient's CVG data and access patient data.Web server 52 can allow access to a user running a web browser. Any webbrowser, co-browser, or other application capable of retrieving contentfrom a network and displaying pages or screens may be used.

Examples of processing devices 18 for interacting within the impedancecardiography system include embedded microprocessors, digital signalprocessors, personal computers, laptop computers, notebook computers,palm top computers, network computers, Internet appliances, or anyprocessor-controlled device capable of storing data, system software 50and any other type of application 48 stored in local memory 46 oraccessible via secondary storage device 54.

Local memory 46 can further include an application for using theknowledge base for heart failure classification in step 39 of FIG. 8.This application is used to provide automated recognition of hemodynamicparameters and waveform attributes. One method includes saving waveformsand hemodynamic parameters in local memory 46 to be used in theaforementioned methods as depicted in FIGS. 5-7 and as templates forfuture comparison and identification of associated disease states. Forexample, a patient having CVG waveform 62 as depicted in FIG. 6 wasdiagnosed by an experienced healthcare professional as having systolicheart failure. A patient having CVG waveform 64 as depicted in FIG. 11was diagnosed by an experienced healthcare professional as havingdiastolic heart failure. These waveforms 62 and 64 are stored aswaveform and hemodynamic data in local memory 46. The method illustratedin FIG. 12 can incorporate the stored waveforms and hemodynamic datadepicted in FIGS. 10 and 11 and includes: measuring CVG waveform andhemodynamic parameters of a new patient 66; providing access to aknowledge base of waveform attributes and hemodynamic parameter datastored in local memory 68; automatically correlating the new patient CVGwaveform attributes and hemodynamic parameters to at least one recordstored in local memory 70; and guiding a goal directed therapy for apossible disease based on this correlation 72.

While FIG. 8 depicts the application for using the knowledge base forheart failure classification included in processing device 18, thoseskilled in the art can appreciate that processing device 18 andknowledge base can be two or more separate systems that communicate withone another via known communication techniques, including but notlimited to modem connections, wireless connections, optical connectionsand the like.

In another embodiment, certain waveform attributes may be learned fromwaveforms associated with disease states, where combinations of theseattributes are used to form a template for that disease state. In yetanother embodiment, analysis and diagnoses for various disease states asdetermined by experienced healthcare professionals can be correlatedwith saved waveforms attributes and hemodynamic parameters. For example,as shown in FIG. 13, an experienced healthcare professional can inputspecific information 74 about waveform 62 (also shown withoutinformation in FIG. 10) correlated with systolic heart failure. Inaddition, as shown in FIG. 14, an experienced healthcare professionalcan input specific information 76 about waveform 64 (also shown withoutinformation in FIG. 11) correlated with diastolic heart failure. Thesewaveforms and hemodynamic data along with additional information can bestored in local memory 46. When a new waveform is generated, it can becompared to the information stored in local memory 46 and healthcareprofessionals can utilize all of the information, as well as thewaveforms and hemodynamic data, to diagnose a possible disease. In thismanner, less experienced healthcare professionals get the benefit ofexperienced healthcare professionals in recognizing and diagnosing apossible disease based on waveform attributes, hemodynamic parametersand/or other information. In addition, recognition and diagnosis of apossible disease can occur quicker based on past diagnoses. The methodcan optionally further provide healthcare professionals with assistancein achieving a goal directed therapy.

While the waveforms depicted in FIGS. 10, 11, 13 and 14 are CVGwaveforms, those skilled in the art can recognize that this method maybe used on any type of waveform. Those skilled in the art can alsorecognize that this method may be used with CVGs that correlate ICGsignals with any signals derived from heart valve activity, hemodynamicevents and any other combination thereof.

While the invention has been described with reference to the specificembodiments thereof, those skilled in the art will be able to makevarious modifications to the described embodiments of the inventionwithout departing from the true spirit and scope of the invention. Theterms and descriptions used herein are set forth by way of illustrationonly and are not meant as limitations. Those skilled in the art willrecognize that these and other variations are possible within the spiritand scope of the invention as defined in the following claims and theirequivalents.

1. A cardiography system for automated recognition of hemodynamicparameters and waveform attributes comprising: at least one sensoradapted to provide at least one waveform signal and a hemodynamicparameter input; a knowledge base configured to provide datacorresponding to various disease states; a processing device in operablecommunication with said at least one sensor and said knowledge base,said processing device configured to receive said at least one waveformsignal and said hemodynamic parameter input, identify waveformattributes on said waveform signal, measure said waveform attributes,and measure said hemodynamic parameter input; cross-reference saidwaveform attributes and said hemodynamic parameter input with saidknowledge base, and output a suggested likelihood of a particulardisease state based on said cross-referencing.
 2. The cardiographysystem of claim 1 further comprising a display device configured todisplay at least said output.
 3. The cardiography system of claim 1wherein said knowledge base further comprises goal directed therapiescorrelated with particular disease states.
 4. The cardiography system ofclaim 3 further comprising said processing device configured to outputat least one suggested goal directed therapy based on the suggestedlikelihood of a particular disease state.
 5. The cardiography system ofclaim I wherein said waveform signal is selected from the groupconsisting of an ICG signal, an ECG signal and a PCG signal.
 6. Thecardiography system of claim 1 wherein said hemodynamic parameter inputis selected from the group consisting of thoracic fluid content, heartrate, pre-ejection period, left ventricular ejection time, systolic timeratio, isovolumic relaxation time, stroke volume, stroke volume index,cardiac output, cardiac index, blood pressure, Heather Index, ratepressure product, ejection fraction, end diastolic volume, pulmonaryartery occlusion pressure, central venous pressure and systemic vascularresistance.
 7. The cardiography system of claim 1 wherein said knowledgebase comprises waveforms, waveform attributes and hemodynamicparameters.
 8. The cardiography system of claim 1 wherein said knowledgebase comprises waveforms, waveform attributes and hemodynamic parametersthat have been associated with systolic heart failure.
 9. Thecardiography system of claim 1 wherein said knowledge base compriseswaveforms, waveform attributes and hemodynamic parameters that have beenassociated with diastolic heart failure.
 10. The cardiography system ofclaim 1 wherein said processing device is selected from the groupconsisting of embedded microprocessors, digital signal processors,personal computers, laptop computers, notebook computers, palm topcomputers, network computers, Internet appliances, andprocessor-controlled devices configured to store data and software. 11.The cardiography system of claim 2 wherein said display device isselected from the group consisting of computer monitor, flat-screendisplay, projector, printing device, and audible device.
 12. Thecardiography system of claim 2 wherein said display device furthercomprises user input devices configured to communicate with said displaydevice and said processing device.
 13. The cardiography system of claim1 further comprising said processing device is configured to create anensemble average waveform based on said waveform signal.
 14. Thecardiography system of claim 13 further comprising said processingdevice is configured to identify and measure waveform attributes on saidensemble average waveform.
 15. The cardiography system of claim 14further comprising said processing device is configured tocross-reference said waveform attributes from said ensemble averagewaveform with data in said knowledge base.
 16. The cardiography systemof claim 15 further comprising said processing device is configured tooutput a suggested likelihood of a particular disease state.
 17. Thecardiography system of claim 1 wherein said at least one sensorcomprises a first sensor adapted to generate said waveform signal and asecond system adapted to provide said hemodynamic parameter input.
 18. Amethod for automated recognition of hemodynamic parameters and waveformattributes to assess disease states comprising: providing at least onesensor for generating at least one waveform signal and a hemodynamicparameter input; providing a knowledge base having data corresponding tovarious disease states; providing a processing device operablycommunicating with said at least one sensor and said knowledge base,said processing device for receiving said at least one waveform signaland said hemodynamic parameter input, identifying waveform attributes onsaid waveform signal, measuring said waveform attributes, measuring saidhemodynamic parameter input, accessing said knowledge base,cross-referencing said waveform attributes with data in said knowledgebase, cross-referencing said hemodynamic parameter input with data insaid knowledge base, and outputting a suggested likelihood of aparticular disease state based on said cross-referencing.
 19. The methodof claim 18 further comprising providing a display device for displayingat least said output.
 20. The method of claim 19 further comprising saidknowledge base further providing goal directed therapies correlated withparticular disease states.
 21. The method of claim 20 further comprisingsaid processing device outputting at least one suggested goal directedtherapy based on the suggested likelihood of a particular disease state.22. The method of claim 18 wherein said waveform signal is selected fromthe group consisting of an ICG signal, an ECG signal and a PCG signal.23. The method of claim 18 wherein said hemodynamic parameter input isselected from the group consisting of thoracic fluid content, heartrate, pre-ejection period, left ventricular ejection time, systolic timeratio, isovolumic relaxation time, stroke volume, stroke volume index,cardiac output, cardiac index, blood pressure, Heather Index, ratepressure product, ejection fraction, end diastolic volume, pulmonaryartery occlusion pressure, central venous pressure and systemic vascularresistance.
 24. The method of claim 18 wherein said knowledge basecomprises waveforms, waveform attributes and hemodynamic parameters. 25.The method of claim 18 wherein said knowledge base comprises waveforms,waveform attributes and hemodynamic parameters that have been associatedwith systolic heart failure.
 26. The method of claim 18 wherein saidknowledge base comprises waveforms, waveform attributes and hemodynamicparameters that have been associated with diastolic heart failure. 27.The method of claim 18 wherein said processing device is selected fromthe group consisting of embedded microprocessors, digital signalprocessors, personal computers, laptop computers, notebook computers,palm top computers, network computers, Internet appliances, andprocessor-controlled devices configured to store data and software. 28.The method of claim 19 wherein said display device is selected from thegroup consisting of computer monitor, flat-screen display, projector,printing device, and audible device.
 29. The method of claim 19 whereinsaid display device further comprises user input devices configured tocommunicate with said display device and said processing device.
 30. Themethod of claim 18 further comprising said processing device creating anensemble average waveform based on said waveform signal.
 31. The methodof claim 30 further comprising said processing device identifying andmeasuring waveform attributes on said ensemble average waveform.
 32. Themethod of claim 31 further comprising said processing devicecross-referencing said waveform attributes from said ensemble averagewaveform with data in said knowledge base.
 33. The method of claim 32further comprising said processing device outputting a suggestedlikelihood of a particular disease state.
 34. The method of claim 18wherein said at least one sensor comprises a first sensor for generatingsaid waveform signal and a second sensor for providing said hemodynamicparameter input.